"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class lowerCamelCase		(_SCREAMING_SNAKE_CASE     ,     unittest.TestCase   ):






					'''simple docstring'''


					a 						=		KandinskyVaaInpaintPipeline
					a 						=		['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
					a 						=		[
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					    '''mask_image''',
					]
					a 						=		[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''latents''',
					    '''guidance_scale''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					a 						=		False



					@property
					def       lowerCAmelCase_       (    self      :					str		)				->							List[Any]:
									return 32



					@property
					def       lowerCAmelCase_       (    self      :					Tuple		)				->							Tuple:
									return 32



					@property
					def       lowerCAmelCase_       (    self      :					int		)				->							Union[str, Any]:
									return self.time_input_dim



					@property
					def       lowerCAmelCase_       (    self      :					Any		)				->							Tuple:
									return self.time_input_dim * 4



					@property
					def       lowerCAmelCase_       (    self      :					Any		)				->							Optional[Any]:
									return 100



					@property
					def       lowerCAmelCase_       (    self      :					Tuple		)				->							Dict:
									torch.manual_seed(0		)

									SCREAMING_SNAKE_CASE__			=					{
									    '''in_channels''': 9,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 8,
									    '''addition_embed_type''': '''image''',
									    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
									    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
									    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
									    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
									    '''layers_per_block''': 1,
									    '''encoder_hid_dim''': self.text_embedder_hidden_size,
									    '''encoder_hid_dim_type''': '''image_proj''',
									    '''cross_attention_dim''': self.cross_attention_dim,
									    '''attention_head_dim''': 4,
									    '''resnet_time_scale_shift''': '''scale_shift''',
									    '''class_embed_type''': None,
									}

									SCREAMING_SNAKE_CASE__			=					UNetaDConditionModel(**__UpperCAmelCase		)
									return model



					@property
					def       lowerCAmelCase_       (    self      :					Any		)				->							List[str]:
									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}



					@property
					def       lowerCAmelCase_       (    self      :					Dict		)				->							Any:
									torch.manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					VQModel(**self.dummy_movq_kwargs		)
									return model



					def       lowerCAmelCase_       (    self      :					Any		)				->							List[str]:
									SCREAMING_SNAKE_CASE__			=					self.dummy_unet
									SCREAMING_SNAKE_CASE__			=					self.dummy_movq

									SCREAMING_SNAKE_CASE__			=					DDIMScheduler(
									    num_train_timesteps=1000 ,			beta_schedule="linear" ,			beta_start=0.0_0085 ,			beta_end=0.012 ,			clip_sample=__UpperCAmelCase ,			set_alpha_to_one=__UpperCAmelCase ,			steps_offset=1 ,			prediction_type="epsilon" ,			thresholding=__UpperCAmelCase ,			)

									SCREAMING_SNAKE_CASE__			=					{
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}

									return components



					def       lowerCAmelCase_       (    self      :					Optional[int] ,			_snake_case      :					List[str] ,			_snake_case      :					List[str]=0		)				->							Tuple:
									SCREAMING_SNAKE_CASE__			=					floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					floats_tensor((1, self.text_embedder_hidden_size) ,			rng=random.Random(seed + 1		)		).to(
									    __UpperCAmelCase		)
									# create init_image
									SCREAMING_SNAKE_CASE__			=					floats_tensor((1, 3, 64, 64) ,			rng=random.Random(__UpperCAmelCase		)		).to(__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					image.cpu().permute(0 ,			2 ,			3 ,			1		)[0]
									SCREAMING_SNAKE_CASE__			=					Image.fromarray(np.uinta(__UpperCAmelCase		)		).convert("RGB"		).resize((256, 256)		)
									# create mask
									SCREAMING_SNAKE_CASE__			=					np.ones((64, 64) ,			dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__			=					0

									if str(__UpperCAmelCase		).startswith("mps"		):
													SCREAMING_SNAKE_CASE__			=					torch.manual_seed(__UpperCAmelCase		)
									else:
													SCREAMING_SNAKE_CASE__			=					torch.Generator(device=__UpperCAmelCase		).manual_seed(__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					{
									    '''image''': init_image,
									    '''mask_image''': mask,
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''generator''': generator,
									    '''height''': 64,
									    '''width''': 64,
									    '''num_inference_steps''': 2,
									    '''guidance_scale''': 4.0,
									    '''output_type''': '''np''',
									}
									return inputs



					def       lowerCAmelCase_       (    self      :					List[str]		)				->							Tuple:
									SCREAMING_SNAKE_CASE__			=					'''cpu'''

									SCREAMING_SNAKE_CASE__			=					self.get_dummy_components()

									SCREAMING_SNAKE_CASE__			=					self.pipeline_class(**__UpperCAmelCase		)
									SCREAMING_SNAKE_CASE__			=					pipe.to(__UpperCAmelCase		)

									pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

									SCREAMING_SNAKE_CASE__			=					pipe(**self.get_dummy_inputs(__UpperCAmelCase		)		)
									SCREAMING_SNAKE_CASE__			=					output.images

									SCREAMING_SNAKE_CASE__			=					pipe(
									    **self.get_dummy_inputs(__UpperCAmelCase		) ,			return_dict=__UpperCAmelCase ,			)[0]

									SCREAMING_SNAKE_CASE__			=					image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE__			=					image_from_tuple[0, -3:, -3:, -1]

									print(F"""image.shape {image.shape}"""		)

									assert image.shape == (1, 64, 64, 3)

									SCREAMING_SNAKE_CASE__			=					np.array(
									    [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848]		)

									assert (
									    np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2
									), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1e-2
									), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



					def       lowerCAmelCase_       (    self      :					List[str]		)				->							str:
									super().test_inference_batch_single_identical(expected_max_diff=3e-3		)



@slow
@require_torch_gpu
class lowerCamelCase		(unittest.TestCase   ):






					'''simple docstring'''


					def       lowerCAmelCase_       (    self      :					Any		)				->							str:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



					def       lowerCAmelCase_       (    self      :					Optional[Any]		)				->							Optional[int]:
									SCREAMING_SNAKE_CASE__			=					load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"		)

									SCREAMING_SNAKE_CASE__			=					load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"		)
									SCREAMING_SNAKE_CASE__			=					np.ones((768, 768) ,			dtype=np.floataa		)
									SCREAMING_SNAKE_CASE__			=					0

									SCREAMING_SNAKE_CASE__			=					'''a hat'''

									SCREAMING_SNAKE_CASE__			=					KandinskyVaaPriorPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-prior" ,			torch_dtype=torch.floataa		)
									pipe_prior.to(__UpperCAmelCase		)

									SCREAMING_SNAKE_CASE__			=					KandinskyVaaInpaintPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-2-decoder-inpaint" ,			torch_dtype=torch.floataa		)
									SCREAMING_SNAKE_CASE__			=					pipeline.to(__UpperCAmelCase		)
									pipeline.set_progress_bar_config(disable=__UpperCAmelCase		)

									SCREAMING_SNAKE_CASE__			=					torch.Generator(device="cpu"		).manual_seed(0		)
									SCREAMING_SNAKE_CASE__			=					pipe_prior(
									    __UpperCAmelCase ,			generator=__UpperCAmelCase ,			num_inference_steps=5 ,			negative_prompt="" ,			).to_tuple()

									SCREAMING_SNAKE_CASE__			=					pipeline(
									    image=__UpperCAmelCase ,			mask_image=__UpperCAmelCase ,			image_embeds=__UpperCAmelCase ,			negative_image_embeds=__UpperCAmelCase ,			generator=__UpperCAmelCase ,			num_inference_steps=100 ,			height=768 ,			width=768 ,			output_type="np" ,			)

									SCREAMING_SNAKE_CASE__			=					output.images[0]

									assert image.shape == (768, 768, 3)

									assert_mean_pixel_difference(__UpperCAmelCase ,			__UpperCAmelCase		)







from ..utils import DummyObject, requires_backends







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
														requires_backends(self    ,						['''speech''']						)







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														requires_backends(self    ,						['''speech''']						)






